The present inventive subject matter relates to a demand data acquisition technology, and more particularly, to a method and an apparatus for providing travel information.
Transportation demand data are crucial to urban transportation planning (such as road planning, subway planning, etc.) as well as transportation facility configuration. Traditionally, transportation demand data acquisition is mainly conducted through paper survey on citizens. Paper survey not only consumes labor and financial resources, but also takes a rather long time to obtain data. Moreover, the data obtained from such a survey are generally static and long-term statistics, which therefore can only be applied to handle long-term issues such as planning and development.
Data acquired in such a way lag behind the current transportation demand and cannot be suitable for various changes. Thus, various kinds of planning, provisions and measures that are made based on these data usually cannot achieve the expected objectives.
Hence, a desire for “dynamic transportation demand,” which means a time varying traffic flow, has become more and more urgent. Dynamic transportation demand is generally influenced by transportation facility and behaviors of people. Dynamic transportation demand is basic information for fine tuning transportation facilities, traffic lights, and short-term transportation policies. While a temporal event occurs, a de-congestion scheme may be designed also based on such information.
It is known that in many countries and regions, the coverage of mobile communication networks has reached at least 90%, and mobile communication devices have become increasingly prevalent. Further, mobile networks can record a user's positions based on cell-towers, which provides a possibility of obtaining a sojourn of people at a specific location. Thus, transportation demand data may be acquired based on the mobile network. Its basic principle is to obtain main positions of people within predetermined regions, for example, “home,” “office,” “school,” “shopping region,” etc., and obtain potential behaviors of the people from the mobility data based on these positions.